The healthcare technology sector has given rise to some of the most innovative startups in the world, which are poised to help people live longer, better lives. The innovations have primarily been driven by the advent of software and mobility, allowing the health sector to digitize many of the pen and paper-based operations and processes that currently slow down service delivery.
More recently, we’re seeing software become far more intelligent and independent. These new capabilities — studied under the banner of artificial intelligence and machine learning — are accelerating the pace of innovation in healthcare. Thus far, the applications of AI and ML in healthcare have enabled the industry to take on some of its biggest challenges in these areas:
Upon close evaluation of the opportunities that exist within each area, it becomes obvious that the stakes are high. As such, those that are first to market with a sustainable product differentiation and value-add will benefit tremendously.
The most significant application of AI and ML in genetics is understanding how DNA impacts life. Although the last several years saw the complete sequencing of the human genome and a mastery of the ability to read and edit it, we still don’t know what most of the genome is actually telling us. Genes are constantly acting out of place in combination with other variables such as food, environment and body types.
If we are to understand what influences life and biology, we must first understand the language that is DNA. This is where ML algorithms come in and the advent of systems such as Google’s Deep Mind and IBM’s Watson. Now, more than ever, it has become possible to digest immense amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans) and perform pattern recognition in a short period of time — which otherwise would have taken a lifetime to complete.
Businesses such as Deep Genomics are making meaningful progress in this realm. The company is developing the capability to interpret DNA by creating a system that predicts the molecular effects of genetic variation. Their database is able to explain how hundreds of millions of genetic variations can impact a genetic code.
Once a better understanding of human DNA is established, there is an opportunity to go one step further and provide personalized insights to individuals based on their idiosyncratic biological dispositions. This trend is indicative of a new era of “personalized genetics,” whereby individuals are able to take full control of their health through access to unprecedented information about their own bodies.
Consumer genetics companies such as 23andMe and Rthm represent a few of the first movers in this domain. They have developed consumerized genetic diagnostic tools to help individuals understand their genetic makeup. With Rthm, users are able to go one step further and leverage the insights produced from their genetic test to implement changes to their everyday routine through a mobile application, all in real time.
As is the case with any application of AI/ML, the technology must have access to vast amounts of data in order to better curate lifestyle changes for individuals. Startups that are focused on mastering the delivery of personal genetics are doing so by considering the following key activities, as highlighted by Japan-based researcher Takashi Kido:
The second point is interesting in that not all genetic information about a patient’s biological predispositions is productive. Being able to control the information in a manner that is conducive to psychological well-being is critical.
Another exciting application of AI/ML in healthcare is the reduction of both cost and time in drug discovery. New drugs typically take 12 to 14 years to make it to market, with the average cost hovering around $2.6 billion.
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